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Abstract
摘要 |
Ever since fission was discovered, it was recognized that the phenomenon can be considered as an evolution of the nuclear shape from that of a single compound nucleus to two receding fragments. Within this conceptual framework, ever more advanced transport treatments have been developed, the most complete treating the shape dynamics as a Langevin process. Such approaches require several ingredients, namely 1) a multi-dimensional potential energy surface as a function of the specified shapes, 2) the corresponding inertial mass tensor, and 3) the dissipation tensor that accounts for the coupling of the shape degrees of freedom to the remainder of the system. While significant progress has been made with regard to the calculation of the potential energy, neither the inertia nor the dissipation is yet well understood and, consequently, it has not been possible to obtain quantitatively useful results for even such basic quantities as the distribution of the fission fragment masses.
However, the situation simplifies significantly if the dissipation can be considered as strong, because then the rate of shape change and the associated accelerations are small and the inertial forces play only a minor role. The shape evolution is then akin to Brownian motion, which can readily be simulated numerically, starting from the excited compound nucleus and stopping when the shape approaches scission and no further change of the mass asymmetry is likely to occur. As it turns out, the resulting fission-fragment mass distribution depends only weakly on the details of the dissipation tensor and particular simplicity emerges if this tensor is isotropic, because then the shape evolution reduces to a simple Metropolis walk on the potential-energy lattice.
Applications of this simple scheme have yielded mass distributions that are in remarkably good agreement with the experimental data. Because the approach is essentially free of adjustable parameters, it can readily be applied to any nucleus for which suitable energy surfaces are available, thus offering unprecedented predictive power. |
